{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-11-06T02:14:38.519889300Z",
     "start_time": "2023-11-06T02:14:05.105416600Z"
    }
   },
   "outputs": [],
   "source": [
    "from torchvision import datasets,transforms\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 导入数据"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "data_transforms = {\n",
    "    'train':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ]),\n",
    "    'test':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ])\n",
    "}\n",
    "import os\n",
    "data_path = 'F:/图像识别数据集/vechs'\n",
    "trainset = datasets.ImageFolder(os.path.join(data_path,'train'),transform=data_transforms['train'])\n",
    "testset = datasets.ImageFolder(os.path.join(data_path,'test'),transform=data_transforms['test'])\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(trainset,batch_size=5,num_workers=4,shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(testset,batch_size=5,num_workers=4,shuffle=True)\n",
    "\n",
    "def imshow(inputs):\n",
    "    inputs = inputs/2+0.5\n",
    "    inputs = inputs.numpy().transpose(1,2,0)\n",
    "    plt.imshow(inputs)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T03:00:55.829641300Z",
     "start_time": "2023-11-06T03:00:55.699641600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "小车\n",
      "小车\n",
      "小车\n",
      "大车\n",
      "大车\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "inputs,labels = next(iter(test_loader))\n",
    "class_names=['大车','小车']\n",
    "imshow(torchvision.utils.make_grid(inputs))\n",
    "for i in labels:\n",
    "    print(class_names[i])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T03:01:02.762885400Z",
     "start_time": "2023-11-06T03:00:57.450249600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## alexnet模型"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlexNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): ReLU(inplace=True)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Dropout(p=0.5, inplace=False)\n",
      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from torchvision import models\n",
    "alexnet = models.alexnet(pretrained=True)\n",
    "print(alexnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T03:11:23.236723Z",
     "start_time": "2023-11-06T03:11:21.697481900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "for p in alexnet.parameters():\n",
    "    p.requires_grad = False\n",
    "\n",
    "alexnet.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(in_features=9216,out_features=4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Linear(4096,2,bias=True)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T03:11:31.423195400Z",
     "start_time": "2023-11-06T03:11:30.926199700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "def train_loop(model,loss_fn,optimizer,train_loader,n_epochs):\n",
    "    for epoch in range(n_epochs):\n",
    "        loss_train = 0.0\n",
    "        for i,data in enumerate(train_loader,0):\n",
    "            imgs,labels = data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            loss = loss_fn(outputs,labels)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            loss_train+=loss.item()\n",
    "            if i==0 or i%10==9:\n",
    "                print('Epoch:{}, i:{},训练损失:{}'.format(epoch+1,i+1,loss_train/len(train_loader)))\n",
    "                loss_train = 0.0"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T03:03:45.256234800Z",
     "start_time": "2023-11-06T03:03:45.239244400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:1,训练损失:0.01211252173439401\n",
      "Epoch:1, i:10,训练损失:0.0965997009980874\n",
      "Epoch:1, i:20,训练损失:0.08653182133299406\n",
      "Epoch:1, i:30,训练损失:0.039071270310487904\n",
      "Epoch:1, i:40,训练损失:0.035090460151922506\n",
      "Epoch:1, i:50,训练损失:0.019602978205094573\n",
      "Epoch:1, i:60,训练损失:0.020426398387453595\n",
      "Epoch:2, i:1,训练损失:6.348171393524428e-05\n",
      "Epoch:2, i:10,训练损失:0.03968191463557515\n",
      "Epoch:2, i:20,训练损失:0.009419827050239336\n",
      "Epoch:2, i:30,训练损失:0.014674998399969495\n",
      "Epoch:2, i:40,训练损失:0.006856784838076192\n",
      "Epoch:2, i:50,训练损失:0.00484003199762512\n",
      "Epoch:2, i:60,训练损失:0.018723113651265253\n"
     ]
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(alexnet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=alexnet.cuda(),loss_fn=loss_fn,optimizer=optimizer,train_loader=train_loader,n_epochs=2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T03:11:51.797305600Z",
     "start_time": "2023-11-06T03:11:35.404309300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "def test_loop(model,test_loader):\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in test_loader:\n",
    "            imgs,labels =  data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            _,preds = torch.max(outputs,dim=1)\n",
    "            total += labels.shape[0]\n",
    "            correct += int((labels==preds).sum())\n",
    "    print('精度:{:.3f}%'.format(correct/total*100))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T03:01:44.402980900Z",
     "start_time": "2023-11-06T03:01:44.387934900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:95.455%\n"
     ]
    }
   ],
   "source": [
    "test_loop(alexnet.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T03:13:42.561031400Z",
     "start_time": "2023-11-06T03:13:36.433372800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Vgg网络"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "vgg = models.vgg16(pretrained=True)\n",
    "print(vgg)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T03:16:57.435058Z",
     "start_time": "2023-11-06T03:16:54.331168600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [],
   "source": [
    "for p in vgg.parameters():\n",
    "    p.requires_grad = False\n",
    "vgg.classifier = nn.Sequential(\n",
    "    nn.Linear(25088,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T03:19:47.622464800Z",
     "start_time": "2023-11-06T03:19:46.713450700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:1,训练损失:0.1358069435494845\n",
      "Epoch:1, i:10,训练损失:0.9134319336687933\n",
      "Epoch:1, i:20,训练损失:0.880225803031296\n",
      "Epoch:1, i:30,训练损失:0.6167639708909832\n",
      "Epoch:1, i:40,训练损失:0.8720390327641221\n",
      "Epoch:1, i:50,训练损失:0.7170956852983256\n",
      "Epoch:1, i:60,训练损失:0.6767652836002287\n",
      "Epoch:2, i:1,训练损失:0.08888358757144114\n",
      "Epoch:2, i:10,训练损失:0.5015308290231423\n",
      "Epoch:2, i:20,训练损失:0.7523634121066234\n",
      "Epoch:2, i:30,训练损失:0.6877717952259251\n",
      "Epoch:2, i:40,训练损失:0.9493073928551595\n",
      "Epoch:2, i:50,训练损失:0.5393060895263172\n",
      "Epoch:2, i:60,训练损失:0.9537696408443763\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(vgg.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=vgg.cuda(),loss_fn=loss_fn,optimizer=optimizer,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T04:19:11.321345600Z",
     "start_time": "2023-11-06T04:17:34.781923900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:100.000%\n"
     ]
    }
   ],
   "source": [
    "test_loop(vgg.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T04:19:58.849882Z",
     "start_time": "2023-11-06T04:19:52.716882600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 残差网络"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet101_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet101_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (6): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (9): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (13): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (14): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (15): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (16): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (17): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (18): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (19): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (20): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (21): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (22): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "resnet = models.resnet101(pretrained=True)\n",
    "print(resnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T04:22:23.630526700Z",
     "start_time": "2023-11-06T04:22:20.265960400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "for p in resnet.parameters():\n",
    "    p.requires_grad = False\n",
    "\n",
    "resnet.fc = nn.Linear(2048,2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T04:27:36.664910800Z",
     "start_time": "2023-11-06T04:27:36.618913800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:1,训练损失:0.012069339634942227\n",
      "Epoch:1, i:10,训练损失:0.10452806216771485\n",
      "Epoch:1, i:20,训练损失:0.08859137531186713\n",
      "Epoch:1, i:30,训练损失:0.03475548412467613\n",
      "Epoch:1, i:40,训练损失:0.031184658286024312\n",
      "Epoch:1, i:50,训练损失:0.07814331212248958\n",
      "Epoch:1, i:60,训练损失:0.06272624555181285\n",
      "Epoch:2, i:1,训练损失:0.0014036059623858967\n",
      "Epoch:2, i:10,训练损失:0.016365305261045206\n",
      "Epoch:2, i:20,训练损失:0.02818981643582954\n",
      "Epoch:2, i:30,训练损失:0.016971155634669007\n",
      "Epoch:2, i:40,训练损失:0.026149941119747083\n",
      "Epoch:2, i:50,训练损失:0.031108360279534685\n",
      "Epoch:2, i:60,训练损失:0.016178182692679226\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(resnet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=resnet.cuda(),n_epochs=2,loss_fn=loss_fn,optimizer=optimizer,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T04:41:36.837878600Z",
     "start_time": "2023-11-06T04:41:09.495328Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:100.000%\n"
     ]
    }
   ],
   "source": [
    "test_loop(model=resnet.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T04:43:28.856585700Z",
     "start_time": "2023-11-06T04:43:22.573436700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## squeezenet"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SqueezeNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (3): Fire(\n",
      "      (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Fire(\n",
      "      (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (6): Fire(\n",
      "      (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Fire(\n",
      "      (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (9): Fire(\n",
      "      (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Fire(\n",
      "      (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Fire(\n",
      "      (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Fire(\n",
      "      (squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Conv2d(512, 1000, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=SqueezeNet1_1_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_1_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "squeezenet = models.squeezenet1_1(pretrained=True)\n",
    "print(squeezenet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T04:58:59.072176Z",
     "start_time": "2023-11-06T04:58:58.953178900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "for p in squeezenet.parameters():\n",
    "    p.requires_grad=False\n",
    "\n",
    "squeezenet.classifier =nn.Sequential(\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Conv2d(512,2,kernel_size=1,stride=1),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.AdaptiveAvgPool2d(output_size=(1,1))\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T05:00:52.521945600Z",
     "start_time": "2023-11-06T05:00:52.501926600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:1,训练损失:0.018717046643866867\n",
      "Epoch:1, i:10,训练损失:0.11988005286357442\n",
      "Epoch:1, i:20,训练损失:0.058500498166826904\n",
      "Epoch:1, i:30,训练损失:0.021830810386626445\n",
      "Epoch:1, i:40,训练损失:0.0070958309387024795\n",
      "Epoch:1, i:50,训练损失:0.003054536365690168\n",
      "Epoch:1, i:60,训练损失:0.04070667130109228\n",
      "Epoch:2, i:1,训练损失:0.0007837773224369424\n",
      "Epoch:2, i:10,训练损失:0.024611734697350958\n",
      "Epoch:2, i:20,训练损失:0.00536933421065695\n",
      "Epoch:2, i:30,训练损失:0.008823939881547064\n",
      "Epoch:2, i:40,训练损失:0.03345578939789433\n",
      "Epoch:2, i:50,训练损失:0.005120610683721292\n",
      "Epoch:2, i:60,训练损失:0.010706192976985981\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(squeezenet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=squeezenet.cuda().eval(),n_epochs=2,loss_fn=loss_fn,train_loader=train_loader,optimizer=optimizer)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T05:02:25.969209500Z",
     "start_time": "2023-11-06T05:02:05.245574300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:95.455%\n"
     ]
    }
   ],
   "source": [
    "test_loop(squeezenet.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T05:03:27.868134500Z",
     "start_time": "2023-11-06T05:03:22.394649400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 做预测"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值---预测值\n",
      "小车 --- 小车\n",
      "小车 --- 小车\n",
      "小车 --- 小车\n",
      "大车 --- 大车\n",
      "大车 --- 大车\n"
     ]
    }
   ],
   "source": [
    "outputs = vgg(inputs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "print('真实值---预测值')\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'---',class_names[j])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T05:07:53.992216500Z",
     "start_time": "2023-11-06T05:07:53.715220400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(torchvision.utils.make_grid(inputs))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T05:16:41.291829Z",
     "start_time": "2023-11-06T05:16:38.804339800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [],
   "source": [
    "torch.save(vgg,'vgg_car_cifar.pkl')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T05:28:55.054782200Z",
     "start_time": "2023-11-06T05:28:46.616939Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "car_cifar = torch.load('vgg_car_cifar.pkl')\n",
    "print(car_cifar)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T05:29:51.174080700Z",
     "start_time": "2023-11-06T05:29:50.145067900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值---预测值\n",
      "小车 --- 小车\n",
      "小车 --- 小车\n",
      "小车 --- 小车\n",
      "大车 --- 大车\n",
      "大车 --- 大车\n"
     ]
    }
   ],
   "source": [
    "outputs = car_cifar(inputs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "print('真实值---预测值')\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'---',class_names[j])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T05:30:18.084868300Z",
     "start_time": "2023-11-06T05:30:17.789868500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值---预测值\n",
      "大车 --- 大车\n",
      "小车 --- 小车\n",
      "大车 --- 大车\n",
      "大车 --- 大车\n",
      "大车 --- 大车\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgs,labels = next(iter(test_loader))\n",
    "outputs = car_cifar(imgs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "print('真实值---预测值')\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'---',class_names[j])\n",
    "imshow(torchvision.utils.make_grid(imgs))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T06:08:01.878850700Z",
     "start_time": "2023-11-06T06:07:56.073860Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
